code: def test_forward_works_with_projection_layer(self):
      params = Params(
          {
              "options_file": self.FIXTURES_ROOT
              / "elmo"
              / "options.json",
              "weight_file": self.FIXTURES_ROOT
              / "elmo"
              / "lm_weights.hdf5",
              "projection_dim": 20,
          }
      )
      word1 = [0] * 50
      word2 = [0] * 50
      word1[0] = 6
      word1[1] = 5
      word1[2] = 4
      word1[3] = 3
      word2[0] = 3
      word2[1] = 2
      word2[2] = 1
      word2[3] = 0
      embedding_layer = ElmoTokenEmbedder.from_params(
          vocab=None, params=params
      )
      assert embedding_layer.get_output_dim() == 20
  
      input_tensor = torch.LongTensor([[word1, word2]])
      embedded = embedding_layer(input_tensor).data.numpy()
      assert embedded.shape == (1, 2, 20)
  
      input_tensor = torch.LongTensor([[[word1]]])
      embedded = embedding_layer(input_tensor).data.numpy()
      assert embedded.shape == (1, 1, 1, 20)
  
file_path: allennlp/tests/modules/token_embedders/elmo_token_embedder_test.py
imports: import torch
  from allennlp.common import Params
  from allennlp.common.testing import ModelTestCase
  from allennlp.data.batch import Batch
  from allennlp.modules.token_embedders import ElmoTokenEmbedder
label_desc: Use image node_id 4 for calling the TestElmoTokenEmbedder obj's underlying member method code with example usage: obj.test_forward_works_with_projection_layer() without return types